Uav urban mobility control: swarm intelligence and collision avoidance
DOI:
https://doi.org/10.30837/2522-9818.2024.4.059Keywords:
unmanned aerial vehicles (UAVs); swarm, collision avoidance; urban drone mobility; integrated management systems.Abstract
Subject matter: Intelligent management of traffic flows in urban environments using swarm intelligence principles and collision avoidance algorithms to ensure safe and efficient urban mobility. Special attention is given to the management of unmanned vehicles and drones. Goal: To develop and analyze an approach to managing urban mobility that combines swarm intelligence principles and collision avoidance algorithms to optimize traffic flows, improve traffic safety, and reduce the number of accidents. Tasks: Investigate the safety and efficiency problems of urban transportation in the context of growing urbanization; develop a model that integrates swarm intelligence and collision avoidance algorithms for managing the movement of unmanned vehicles; conduct a series of experiments to test the effectiveness of the proposed approach; analyze the results of the experiments and determine the potential for improving urban mobility and ensuring road safety. Methods: Mathematical modeling of traffic flows using the swarm intelligence algorithm to coordinate the movement of unmanned vehicles and avoid collisions. Results: The proposed urban mobility management algorithm has demonstrated the ability to improve traffic flows, reduce the risk of collisions, and increase overall road safety. The results of the experiments confirmed the effectiveness of using swarm intelligence for coordination vehicles and collision avoidance algorithms to prevent accidents.
References
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Na, S., Niu, H., Lennox, B. and Arvin, F. (2022), "Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning," in IEEE Transactions on Vehicular Technology, Vol. 71, No. 3, Р. 2511-2526. DOI: 10.1109/TVT.2022.3145346.
Pawełczyk, M. Ł., Wojtyra, M. (2020), "Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection," in IEEE Access, Vol. 8, Р. 174394-174409. DOI: 10.1109/ACCESS.2020.3026192
Sytsma, J., Thompson, D., and Sicoli, J. (2023), "Drone Ultrasonic Detection", Australian International Aerospace Congress. available online: https://search.informit.org/doi/abs/10.3316/informit.063769306863002 (last accessed 11 November 2024).
Petersen, K. (2021), "Tackling air pollution with autonomous drones," MIT School of Engineering. available online: https://news.mit.edu/2021/tackling-air-pollution-with-autonomous-drones-0624 (last accessed 11 November 2024).
Chu, J. (2023), "New 'traffic cop' algorithm helps a drone swarm stay on task," MIT News Office. available online: https://news.mit.edu/2023/new-traffic-cop-algorithm-drone-swarm-wireless-0313 (last accessed 11 November 2024).
Enwerem, C. and Baras, J.S. (2023), "Consensus-Based Leader-Follower Formation Tracking for Control-Affine Nonlinear Multiagent Systems," available online: https://doi.org/10.48550/arXiv.2309.09156
Xu, Z., Yan, T., Yang, S.X., and Gadsden, S.A. (2023), "Distributed Leader Follower Formation Control of Mobile Robots based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter", IEEE Transactions on Industrial Informatics. DOI: 10.1109/TII.2023.3272666
Ye, Y., Hu, S., Zhu, X., and Sun, Z. (2024), "An Improved Super-Twisting Sliding Mode Composite Control for Quadcopter UAV Formation", Machines, 12(1), 32 р. DOI: https://doi.org/10.3390/machines12010032
Sarhadi, P., et al. (2023), "Adaptive formation motion planning and control of autonomous underwater vehicles using deep reinforcement learning", IEEE Journal of Oceanic Engineering. DOI: 10.48550/arXiv.2304.00225
Zhang, J., et al. (2019), "Perdix: A Swarm of Swarming UAVs", Journal of Field Robotics, Vol. 36, No. 6, Р. 1240–1255.
"Distributed Leader Follower Formation Control of Mobile Robots based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter". 2023. 19 р. available at: https://arxiv.org/pdf/2301.01234.pdf
Sytsma, J., Thompson, D., and Sicoli, J. (2023), "Drone Ultrasonic Detection", Australian International Aerospace Congress. Р. 221–225. available at: https://search.informit.org/doi/abs/10.3316/informit.063769306863002
Kosanam, B., Kukkadapu, A. (2017), Swarm Intelligence Based Traffic Control System. IRA-International Journal of Technology & Engineering. Р. 46-52. DOI: http://dx.doi.org/10.21013/jte.v7.n2.p5
Tahir, A. (2023), "Formation Control of Swarms of Unmanned Aerial Vehicles", Doctoral Dissertation, University of Turku, Turku, Finland. 449 р. available at: https://urn.fi/URN:ISBN:978-951-29-9411-3
Pathak, R., Barzin, R. and Bora, G.C. (2018), "Data-driven precision agricultural applications using field sensors and Unmanned Aerial Vehicle", International Journal of Precision Agriculture and Aviation, Vol. 1(1), P. 19–23. DOI: https://doi.org/10.33440/j.ijpaa.20180101.0004
Ali, Z.A. (2024), "Introductory chapter: Motion planning for dynamic agents", InTechOpen. available at: https://www.intechopen.com/chapters/1178552
Gielis, J., Shankar, A. and Prorok, A. (2022), "A Critical Review of Communications in Multi-robot Systems", Current Robot Reports, Vol. 3(3), P. 213–225. DOI: 10.1007/s43154-022-00090-9
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